A New Search for Neutrino Point Sources with IceCube

TL;DR

This paper introduces a refined search method for neutrino point sources using IceCube data, employing advanced statistical and machine learning techniques to enhance localization and flux estimation, thereby increasing discovery potential.

Contribution

The study develops a new analysis framework with precise numerical PSFs and machine learning estimates, improving source localization and flux characterization over previous methods.

Findings

Up to 30% increased discovery potential.

Enhanced source localization accuracy.

Improved flux estimates for neutrino sources.

Abstract

The IceCube Neutrino Observatory, deployed inside the deep glacial ice at the South Pole, is the largest neutrino telescope in the world. While eight years have passed since IceCube discovered a diffuse flux of high-energy astrophysical neutrinos, the sources of the vast majority of these neutrinos remain unknown. Here, we present a new search for neutrino point sources that improves the accuracy of the statistical analysis, especially in the low energy regime. We replaced the usual Gaussian approximations of IceCube's point spread function with precise numerical representations, obtained from simulations, and combined them with new machine learning-based estimates of event energies and angular errors. Depending on the source properties, the new analysis provides improved source localization, flux characterization and thereby discovery potential (by up to 30%) over previous works. The analysis will be applied to IceCube data that has been recorded with the full 86-string detector configuration from 2011 to 2020 and includes improved detector calibration.